Combining Clustering and a Decision Tree Classifier in a Forecasting Task

Automatic Control and Computer Science 2010
Arnis Kiršners, Sergejs Paršutins, Arkādijs Borisovs

A joint analysis of continuous (time series demand observations) and discrete (well-describing parameters) data is studied. Such data mining techniques as data collection, preprocessing, clustering analysis, and classification are considered. Upon continuous data preprocessing and clustering, images of possible sales development are constructed. A new product’s demand is searched for using inductive decision trees built on well-describing data.

Atslēgas vārdi
Time series, clusterization, classification, decision trees, model's of forecasting
DOI
10.3103/S0146411610030028
Hipersaite
http://www.springerlink.com/content/gk3m1051t615445x/

Kiršners, A., Paršutins, S., Borisovs, A. Combining Clustering and a Decision Tree Classifier in a Forecasting Task. Automatic Control and Computer Science, 2010, Vol. 44, No. 3, 124.-132.lpp. ISSN 0146-4116. Pieejams: doi:10.3103/S0146411610030028

Publikācijas valoda
English (en)